I have dataframe
(index) purchased sold price
2013-04-04 14.865494 14.800361 12.762369
2013-04-05 15.191654 15.296572 12.777120
2013-04-06 15.402671 15.844089 12.773146
2013-04-07 15.840517 15.437765 12.780774
and the indices are df.index:
DatetimeIndex(['2013-04-04', '2013-04-05', '2013-04-06', '2013-04-07',
'2013-04-08', '2013-04-09', '2013-04-11', '2013-04-12',
'2013-04-14', '2013-04-15',
dtype='datetime64[ns]', name='date',length=273,freq=None)
I need to write a model to reduce seasonality in the data using decomposing: i wrote this piece of code for:
from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(df)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid
but it raise an error for decomposition = seasonal_decompose(df)
ValueError: You must specify a freq or x must be a pandas object with a timeseries index
What is the problem? Is there any other way to do this?
